Ramlall, V. | Search bioRxiv

bioRxiv2019

Predicting the genetic ancestry of 2.6 million New York City patients using clinical data

Ancestry is an essential covariate in clinical genomics research. When genetic data are available, dimensionality reduction techniques, such as principal components analysis, are used to determine ancestry in complex populations. Unfortunately, these data are not always available in the clinical and research settings. For example, electronic health records (EHRs), which are a rich source of temporal human disease data that could be used to enhance genetic studies, do not directly capture ancestry. Here, we present a novel algorithm for predicting genetic ancestry using only variables that are routinely captured in EHRs, such as self-reported race and ethnicity, and condition billing codes. Using patients that have both genetic and clinical information at Columbia University/ New York-Presbyterian Irving Medical Center, we developed a pipeline that uses only clinical data to predict the genetic ancestry of all patients of which more than 80% identify as other or unknown. Our ancestry estimates can be used in observational studies of disease inheritance, to guide genetic cohort studies, or to explore health disparities in clinical care and outcomes.

bioinformatics↗

bioRxiv2018

Biophysical Principles of Lineage Factor PU.1 Binding Revealed by NextPBMs

Determining the biophysical principles that shape transcription factor (TF) binding in a cell-specific manner is key to quantitative models of gene expression. High-throughput (HT) in vitro methods measuring protein-DNA binding are invaluable for relating TF binding affinity to genome-wide binding; however, the impact of cell-specific post-translational modifications (PTMs) and cofactors are not routinely assessed. To address these limitations, we describe a new HT approach, called nextPBMs (nuclear extract protein-binding microarrays), to characterize TF binding that accounts for PTMs and endogenous cofactors. We use nextPBMs to examine the DNA binding of the lineage factor PU.1/Spi1 and IRF8 in human monocytes. We identify two binding modes for PU.1 in monocytes - autonomous binding unaffected by PTMs and cooperative binding with IRF8, and identify a single cooperative mode for IRF8. We characterize the DNA binding of PU.1:IRF8 complexes, and show how nextPBMs can be used to discover cell-specific cofactors and characterize TF cooperativity at single-nucleotide resolution. We show that chromatin state and cofactors both influence the affinity requirements for PU.1 binding sites. Furthermore, we find that the influences of cooperative (IRF8) and collaborative (C/EBP) cofactors on PU.1-binding-site affinity are independent and additive.

molecular biology↗